Diagnostic - one model
Erica Goto
2/10/2019
Library & Dataset
Using OLR
Inspect Dataset Using Training and Validation
OLR Equations
- Using all variables
Inspect Dataset Using Training and Validation
- Training set divide in the Diagnostic_based.on.Harell.Rmd file
vclust <- varclus (~angle+brick+wood+mixed+ density+EN +TC + TC_mature_soil + TC_saprolite_soil + TC_weath_rock + TC_unstable_structure + T_construction + spring + landfill + garbage + crack + leaning_wall + scars + downward_floor + tilted + fracture + conc_rainfall + wastewater + leak + septic_tank + tree + ground_veg + deforestation + banana + drainage , data=train.data)
# took out density since training has 0 d4 and it was not allowing do the plot
p <- plot(vclust)
par(mfrow=c(6,5))
plot.xmean.ordinaly (risk~angle+brick+wood+mixed+ density+EN +TC + TC_mature_soil + TC_saprolite_soil + TC_weath_rock + TC_unstable_structure + T_construction + spring + landfill + garbage + crack + leaning_wall + scars + downward_floor + tilted + fracture + conc_rainfall + wastewater + leak + septic_tank + tree + ground_veg + deforestation + banana + drainage, data=train.data, cr=TRUE , subn=FALSE)
#angle + building+density+EN +TC + TC_mature_Soil + TC_saprolito + TC_weath_rock + TC_rock + TC_geol_desfav + Taterro + DepEncNatural + DepTaludeAterro + DepTaludeCorte + DepTaludeAterro + aterro + lixo + entulho + crack + belly_wall + scars + drawback + tilted + fracture + conc_rainfall_water + wastewater + leak + septic_tank + drainage + tree + ground_veg + deforestation + banana Diagnostic 2: Proportion (-5% of one of the parameters based on what is expected. Since some parameters have 2 predictors, others 5)
#library(plyr)
brick <- count(train.data$brick) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "brick")
wood <- count(train.data$wood) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "wood")
mixed <- count(train.data$mixed) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "mixed")
TC_mature_soil <- count(train.data$TC_mature_soil) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "TC_mature_soil")
T_construction <- count(train.data$T_construction ) %>%
mutate ("Percentage"=(freq/265)*100) %>%
mutate("Classifier" = "T_construction ")
spring <- count(train.data$spring) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "spring")
landfill <- count(train.data$landfill) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "landfill")
garbage <- count(train.data$garbage) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "garbage")
crack <- count(train.data$crack) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "crack")
leaning_wall <- count(train.data$leaning_wall) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "leaning_wall")
scars <- count(train.data$scars) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "DepTaludeAterro")
downward_floor <- count(train.data$downward_floor) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "scars")
tilted <- count(train.data$tilted) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "tilted")
conc_rainfall <- count(train.data$conc_rainfall) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "conc_rainfall")
wastewater <- count(train.data$wastewater) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "wastewater")
leak <- count(train.data$leak) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "conc_rainfall_water")
septic_tank <- count(train.data$septic_tank) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "septic_tank")
angle <- count(train.data$angle) # angle A less than 5% but the rest are okay (3,50, 91, 277, 109) Expected=106
angle <- angle %>%
mutate("Percentage"=(freq/106)*100)%>%
mutate("Classifier" = "angle")
EN <- count(train.data$EN) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "EN")
TC <- count(train.data$TC) %>%
mutate ("Percentage"=(freq/265)*100) %>%
mutate("Classifier" = "TC")
TC_saprolite_soil <- count(train.data$TC_saprolite_soil ) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "TC_saprolite_soil ")
banana <- count(train.data$banana) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "banana")
drainage <- count(train.data$drainage) %>%
mutate ("Percentage"=(freq/176.7)*100)%>%
mutate("Classifier" = "drainage")
deforestation <- count(train.data$deforestation) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "deforestation")
TC_unstable_structure <- count(train.data$TC_unstable_structure ) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "TC_unstable_structure ")
tree <- count(train.data$tree) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "tree")
ground_veg <- count(train.data$ground_veg) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "ground_veg")
density <- count(train.data$density) %>% #(79, 415, 36) # d4 =0
mutate ("Percentage"=(freq/132.5)*100)%>%
mutate("Classifier" = "density")
TC_weath_rock <- count(train.data$TC_weath_rock ) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "TC_weath_rock ")
fracture <- count(train.data$fracture) %>%
mutate ("Percentage"=(freq/265)*100)%>%
mutate("Classifier" = "fracture")
df <- rbind(brick, wood, mixed, TC_mature_soil, T_construction, spring, landfill, garbage, crack, leaning_wall, scars, downward_floor, tilted, conc_rainfall, wastewater, leak, septic_tank, angle, EN, TC, TC_saprolite_soil, banana, drainage, deforestation, TC_unstable_structure, tree, ground_veg,density, TC_weath_rock, fracture)
df## x freq Percentage Classifier
## 1 FALSE 33 12.4528302 brick
## 2 TRUE 497 187.5471698 brick
## 3 FALSE 458 172.8301887 wood
## 4 TRUE 72 27.1698113 wood
## 5 FALSE 495 186.7924528 mixed
## 6 TRUE 35 13.2075472 mixed
## 7 FALSE 264 99.6226415 TC_mature_soil
## 8 TRUE 266 100.3773585 TC_mature_soil
## 9 FALSE 213 80.3773585 T_construction
## 10 TRUE 317 119.6226415 T_construction
## 11 FALSE 513 193.5849057 spring
## 12 TRUE 17 6.4150943 spring
## 13 FALSE 329 124.1509434 landfill
## 14 TRUE 201 75.8490566 landfill
## 15 FALSE 353 133.2075472 garbage
## 16 TRUE 177 66.7924528 garbage
## 17 FALSE 443 167.1698113 crack
## 18 TRUE 87 32.8301887 crack
## 19 FALSE 500 188.6792453 leaning_wall
## 20 TRUE 30 11.3207547 leaning_wall
## 21 FALSE 329 124.1509434 DepTaludeAterro
## 22 TRUE 201 75.8490566 DepTaludeAterro
## 23 FALSE 468 176.6037736 scars
## 24 TRUE 62 23.3962264 scars
## 25 FALSE 424 160.0000000 tilted
## 26 TRUE 106 40.0000000 tilted
## 27 FALSE 20 7.5471698 conc_rainfall
## 28 TRUE 510 192.4528302 conc_rainfall
## 29 FALSE 205 77.3584906 wastewater
## 30 TRUE 325 122.6415094 wastewater
## 31 FALSE 342 129.0566038 conc_rainfall_water
## 32 TRUE 188 70.9433962 conc_rainfall_water
## 33 FALSE 525 198.1132075 septic_tank
## 34 TRUE 5 1.8867925 septic_tank
## 35 C 30 28.3018868 angle
## 36 D 131 123.5849057 angle
## 37 E 369 348.1132075 angle
## 38 FALSE 337 127.1698113 EN
## 39 TRUE 193 72.8301887 EN
## 40 FALSE 31 11.6981132 TC
## 41 TRUE 499 188.3018868 TC
## 42 FALSE 444 167.5471698 TC_saprolite_soil
## 43 TRUE 86 32.4528302 TC_saprolite_soil
## 44 FALSE 361 136.2264151 banana
## 45 TRUE 169 63.7735849 banana
## 46 Y 71 40.1810979 drainage
## 47 P 231 130.7300509 drainage
## 48 N 228 129.0322581 drainage
## 49 FALSE 493 186.0377358 deforestation
## 50 TRUE 37 13.9622642 deforestation
## 51 FALSE 517 195.0943396 TC_unstable_structure
## 52 TRUE 13 4.9056604 TC_unstable_structure
## 53 FALSE 210 79.2452830 tree
## 54 TRUE 320 120.7547170 tree
## 55 FALSE 160 60.3773585 ground_veg
## 56 TRUE 370 139.6226415 ground_veg
## 57 d1 66 49.8113208 density
## 58 d2 424 320.0000000 density
## 59 d3 40 30.1886792 density
## 60 FALSE 521 196.6037736 TC_weath_rock
## 61 TRUE 9 3.3962264 TC_weath_rock
## 62 FALSE 529 199.6226415 fracture
## 63 TRUE 1 0.3773585 fractureTC_weath_rock, TC_rock_TC_geol_desf, fracture, TC_rock
- OF: angle (slope angle), building (building material), EN (natural slope), TC_saprolito (cut slope with saprolite), Taterro (slope with landfill), DepEncostaNatural (natural slope with construction material), DepTaludeAterro (slope with landfill and construction material), DepTaludeCorte (cut slope with construction material), landfill, garbage, construction_deposit (construction material), crack, leaning_wall, scars, downward_floor, tilted, conc_rainfall_water (concentration of rainfall water), wastewater, leak, drainage
- MOF:TC_mature_soil (slope cut with soil), density, TC (slope cut), - - TC_geol_desfav (cut slope with geology favorable to instability), tree, ground_veg (ground vegetation), deforestation, banana
- NOF: TC_weath_rock (cut slope with weathering rock), TC_rock (cut slope in bedrock), fracture, septic_tank
- GP:all classifiers minus BP classifiers
- BP: TC_weath_rock (True N= 11 , False N= 529), TC_geol_desf (True N= 11 , False N= 529), fracture (True N=1 , False N=529), TC_rock (True N=1 , False N=529 ), septic_tank (True N=4 , False N=526 )
Equation 1
- consider all parameter okay from first diagnostic:
f1 <- lrm(risk ~ building + EN + TC_saprolito + Taterro + DepEncNatural + DepTaludeAterro + DepTaludeCorte + landfill + garbage + construction_deposit + crack + leaning_wall + scars + downward_floor +tilted + conc_rainfall_water + wastewater + leak + tree + ground_veg + banana , data=train.data, x=TRUE , y=TRUE)
f1 <- lrm(risk ~ building + EN + TC_saprolito + Taterro + DepEncNatural + DepTaludeAterro + DepTaludeCorte + landfill + garbage + construction_deposit + crack + leaning_wall + scars + downward_floor +tilted + conc_rainfall_water + wastewater + leak + tree + ground_veg + banana + septic_tank +TC_mature_Soil , data=train.data, x=TRUE , y=TRUE) print (f1 , latex =TRUE , coefs =5) stargazer(anova(f1), type=“text”, style=“default”)
# Equation 1
eq_OLR_01 <- polr(risk ~ brick+ wood+ EN + TC_mature_soil + T_construction + spring+ landfill+ leak+ garbage+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ septic_tank+ conc_rainfall+ wastewater+ ground_veg + angle + TC_saprolite_soil, data= train.data
, method = "logistic", Hess = TRUE)
ctable <- coef(summary(eq_OLR_01))
p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )
ctable## Value Std. Error t value p value
## brickTRUE -0.62286890 0.4531264 -1.3746029 8.462729e-02
## woodTRUE 1.14420641 0.3311403 3.4553522 2.747872e-04
## ENTRUE 0.68534436 0.3596507 1.9055831 2.835216e-02
## TC_mature_soilTRUE 0.70731286 0.2215144 3.1930791 7.038218e-04
## T_constructionTRUE 0.19270082 0.3528399 0.5461425 2.924840e-01
## springTRUE -0.24198349 0.6249096 -0.3872296 3.492931e-01
## landfillTRUE 0.24950144 0.3240544 0.7699370 2.206686e-01
## leakTRUE -0.33603567 0.2343302 -1.4340262 7.578240e-02
## garbageTRUE -0.00333971 0.2857214 -0.0116887 4.953370e-01
## crackTRUE 1.83902211 0.3245525 5.6663309 7.294382e-09
## leaning_wallTRUE 1.45470642 0.4781146 3.0425897 1.172760e-03
## scarsTRUE 3.63364977 0.3453973 10.5202039 3.486128e-26
## downward_floorTRUE 1.32342387 0.3553109 3.7246924 9.777676e-05
## tiltedTRUE 0.88083008 0.3038794 2.8986170 1.874062e-03
## septic_tankTRUE 0.36805649 1.0507361 0.3502844 3.630626e-01
## conc_rainfallTRUE 1.50842426 0.5154283 2.9265455 1.713747e-03
## wastewaterTRUE 0.78261548 0.2299758 3.4030344 3.332094e-04
## ground_vegTRUE 0.92544577 0.2478727 3.7335525 9.439893e-05
## angleD 0.60222299 0.4638569 1.2982947 9.709305e-02
## angleE 0.77028670 0.5317272 1.4486503 7.371763e-02
## TC_saprolite_soilTRUE 0.21591110 0.2785599 0.7750977 2.191410e-01
## R1|R2 1.08026855 0.8553825 1.2629070 1.033113e-01
## R2|R3 5.21764155 0.9041763 5.7706020 3.949442e-09
## R3|R4 10.08231484 1.0113269 9.9693929 1.037466e-23stargazer((ctable), type="text", style="default", digits = 2)##
## =======================================================
## Value Std. Error t value p value
## -------------------------------------------------------
## brickTRUE -0.62 0.45 -1.37 0.08
## woodTRUE 1.14 0.33 3.46 0.0003
## ENTRUE 0.69 0.36 1.91 0.03
## TC_mature_soilTRUE 0.71 0.22 3.19 0.001
## T_constructionTRUE 0.19 0.35 0.55 0.29
## springTRUE -0.24 0.62 -0.39 0.35
## landfillTRUE 0.25 0.32 0.77 0.22
## leakTRUE -0.34 0.23 -1.43 0.08
## garbageTRUE -0.003 0.29 -0.01 0.50
## crackTRUE 1.84 0.32 5.67 0
## leaning_wallTRUE 1.45 0.48 3.04 0.001
## scarsTRUE 3.63 0.35 10.52 0
## downward_floorTRUE 1.32 0.36 3.72 0.0001
## tiltedTRUE 0.88 0.30 2.90 0.002
## septic_tankTRUE 0.37 1.05 0.35 0.36
## conc_rainfallTRUE 1.51 0.52 2.93 0.002
## wastewaterTRUE 0.78 0.23 3.40 0.0003
## ground_vegTRUE 0.93 0.25 3.73 0.0001
## angleD 0.60 0.46 1.30 0.10
## angleE 0.77 0.53 1.45 0.07
## TC_saprolite_soilTRUE 0.22 0.28 0.78 0.22
## R1| R2 1.08 0.86 1.26 0.10
## R2| R3 5.22 0.90 5.77 0
## R3| R4 10.08 1.01 9.97 0
## -------------------------------------------------------less p-value = 0.10 (TC_saprolitoTRUE,TaterroTRUE, DepTaludeAterroTRUE,DepTaludeAterroTRUE,landfillTRUE, construction_depositTRUE, leakTRUE)
par(mfrow=c(5,4))
plot.xmean.ordinaly (risk~ brick+ wood+ EN + TC_mature_soil + T_construction + spring+ landfill+ leak+ garbage+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ septic_tank+ conc_rainfall+ wastewater+ ground_veg + angle + TC_saprolite_soil
,data=train.data, cr=TRUE , subn=FALSE , cex.lab=1.5, cex.axis=2, cex.sub=2, cex.main=2)
Creating function with four level
Equation 1
sf <- function (y) {
c('y>=1' = qlogis(mean(y>=1)),
'y>=2' = qlogis(mean(y>=2)),
'y>=3' = qlogis(mean(y>=3)),
'y>=4' = qlogis(mean(y>=4)))
}
s<-with(train.data, summary(as.numeric(risk)~brick+ wood+ EN + TC_mature_soil + T_construction + spring+ landfill+ leak+ garbage+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ septic_tank+ conc_rainfall+ wastewater+ ground_veg + angle + TC_saprolite_soil
, fun=sf))
s## as.numeric(risk) N= 529 , 1 Missing
##
## +-----------------+---+---+----+----------+------------+----------+
## | | |N |y>=1|y>=2 |y>=3 |y>=4 |
## +-----------------+---+---+----+----------+------------+----------+
## |brick |No | 33|Inf | 2.7408400| 0.980829253|-0.6931472|
## | |Yes|496|Inf | 2.3048048|-0.064538521|-2.0414481|
## +-----------------+---+---+----+----------+------------+----------+
## |wood |No |457|Inf | 2.2143609|-0.171094494|-2.1899518|
## | |Yes| 72|Inf | 3.5553481| 1.174119841|-0.8209806|
## +-----------------+---+---+----+----------+------------+----------+
## |EN |No |336|Inf | 1.8666608|-0.498147166|-2.3595519|
## | |Yes|193|Inf | 4.5591262| 0.894629235|-1.3733910|
## +-----------------+---+---+----+----------+------------+----------+
## |TC_mature_soil |No |264|Inf | 1.9810015|-0.243622083|-2.2141741|
## | |Yes|265|Inf | 2.8134107| 0.235038339|-1.6695022|
## +-----------------+---+---+----+----------+------------+----------+
## |T_construction |No |213|Inf | 1.6610253|-0.822825004|-3.0106209|
## | |Yes|316|Inf | 3.1487834| 0.531130890|-1.4925166|
## +-----------------+---+---+----+----------+------------+----------+
## |spring |No |512|Inf | 2.2918898|-0.031252544|-2.0193376|
## | |Yes| 17|Inf | Inf| 0.875468737|-0.1177830|
## +-----------------+---+---+----+----------+------------+----------+
## |landfill |No |328|Inf | 1.8132657|-0.550046337|-2.5848176|
## | |Yes|201|Inf | 5.2983174| 0.926762032|-1.2431935|
## +-----------------+---+---+----+----------+------------+----------+
## |leak |No |341|Inf | 1.9627740|-0.253549066|-2.3385955|
## | |Yes|188|Inf | 3.6000482| 0.454472687|-1.3730491|
## +-----------------+---+---+----+----------+------------+----------+
## |garbage |No |353|Inf | 2.2067033|-0.198953957|-2.3057052|
## | |Yes|176|Inf | 2.6149598| 0.391280473|-1.3581235|
## +-----------------+---+---+----+----------+------------+----------+
## |crack |No |442|Inf | 2.1772738|-0.338023827|-2.6561518|
## | |Yes| 87|Inf | 3.7495041| 2.436116486|-0.2076394|
## +-----------------+---+---+----+----------+------------+----------+
## |leaning_wall |No |499|Inf | 2.2635346|-0.100284363|-2.0884914|
## | |Yes| 30|Inf | Inf| 2.197224577|-0.2682640|
## +-----------------+---+---+----+----------+------------+----------+
## |scars |No |328|Inf | 1.8132657|-1.286396208|-4.3944492|
## | |Yes|201|Inf | 5.2983174| 3.183248647|-0.7610978|
## +-----------------+---+---+----+----------+------------+----------+
## |downward_floor |No |467|Inf | 2.1901071|-0.254034038|-2.2885333|
## | |Yes| 62|Inf | Inf| 3.401197382|-0.3920421|
## +-----------------+---+---+----+----------+------------+----------+
## |tilted |No |423|Inf | 2.0794415|-0.437088297|-2.4696392|
## | |Yes|106|Inf | Inf| 2.505525937|-0.7073318|
## +-----------------+---+---+----+----------+------------+----------+
## |septic_tank |No |524|Inf | 2.3173689|-0.015267472|-1.9372144|
## | |Yes| 5|Inf | Inf| 1.386294361|-0.4054651|
## +-----------------+---+---+----+----------+------------+----------+
## |conc_rainfall |No | 20|Inf |-0.2006707|-2.944438979| -Inf|
## | |Yes|509|Inf | 2.5755765| 0.066822496|-1.8695372|
## +-----------------+---+---+----+----------+------------+----------+
## |wastewater |No |205|Inf | 1.6875557|-0.529079064|-2.7777835|
## | |Yes|324|Inf | 3.0252911| 0.323787077|-1.5656353|
## +-----------------+---+---+----+----------+------------+----------+
## |ground_veg |No |160|Inf | 1.2729657|-1.132228899|-2.5123056|
## | |Yes|369|Inf | 3.3928291| 0.446287103|-1.7208515|
## +-----------------+---+---+----+----------+------------+----------+
## |angle |C | 30|Inf | Inf|-0.546543706|-3.3672958|
## | |D |131|Inf | 4.1666652| 0.970357953|-1.3958638|
## | |E |368|Inf | 1.9709898|-0.284512498|-2.0763881|
## +-----------------+---+---+----+----------+------------+----------+
## |TC_saprolite_soil|No |443|Inf | 2.2047718|-0.076787143|-2.0845306|
## | |Yes| 86|Inf | 3.3202283| 0.376477571|-1.2602536|
## +-----------------+---+---+----+----------+------------+----------+
## |Overall | |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +-----------------+---+---+----+----------+------------+----------+plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=1, cex.axis=1, cex.sub=1)
Equation 2
- parameters okay and so/so
- porportion
- excluded TC_geol_desf
f2 <- lrm(risk ~ angle + building + EN + TC_saprolito + Taterro + DepEncNatural + DepTaludeAterro + DepTaludeCorte + landfill + garbage + construction_deposit + crack + leaning_wall + scars + downward_floor +tilted + conc_rainfall_water + wastewater + leak + drainage + TC_mature_Soil + density + TC + tree +ground_veg + deforestation + banana , data=train.data, x=TRUE , y=TRUE)
stargazer(anova(f2), type="text", style="default")eq_OLR_02 <- polr(risk ~ brick+ wood+ EN+ TC_mature_soil+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ mixed+ conc_rainfall+ wastewater+ angle+ banana+ drainage+ TC_saprolite_soil+ TC+ deforestation,
data= train.data
, method = "logistic", Hess = TRUE)
ctable <- coef(summary(eq_OLR_02))
p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )
ctable## Value Std. Error t value p value
## brickTRUE -0.69036422 0.5569614 -1.23951903 1.075767e-01
## woodTRUE 0.91430850 0.3457010 2.64479537 4.087017e-03
## ENTRUE 0.48675639 0.3796152 1.28223619 9.987990e-02
## TC_mature_soilTRUE 0.61769494 0.2338608 2.64129325 4.129510e-03
## T_constructionTRUE 0.26126106 0.3595060 0.72672239 2.336980e-01
## landfillTRUE 0.21539108 0.3277797 0.65712155 2.555514e-01
## leakTRUE -0.47912088 0.2411871 -1.98651152 2.348828e-02
## garbageTRUE -0.05829547 0.2914945 -0.19998823 4.207449e-01
## crackTRUE 1.95844874 0.3317082 5.90413065 1.772556e-09
## leaning_wallTRUE 1.59434661 0.4922470 3.23891598 5.999246e-04
## treeTRUE 0.02374714 0.2374989 0.09998839 4.601768e-01
## downward_floorTRUE 1.17479081 0.3591213 3.27129285 5.352849e-04
## tiltedTRUE 0.76950621 0.3065053 2.51058007 6.026649e-03
## ground_vegTRUE 0.70130555 0.2665432 2.63111439 4.255269e-03
## scarsTRUE 3.59524590 0.3507263 10.25085998 5.864762e-25
## mixedTRUE -0.27473965 0.5351857 -0.51335387 3.038519e-01
## conc_rainfallTRUE 1.02955570 0.5464442 1.88410035 2.977570e-02
## wastewaterTRUE 0.56515040 0.2379838 2.37474310 8.780584e-03
## angleD 0.51086114 0.4709446 1.08475843 1.390143e-01
## angleE 0.74168257 0.5379186 1.37880078 8.397809e-02
## bananaTRUE 0.52107386 0.2472134 2.10779007 1.752458e-02
## drainage.L 1.23970914 0.2803223 4.42244285 4.879558e-06
## drainage.Q -0.15175369 0.1872887 -0.81026621 2.088936e-01
## TC_saprolite_soilTRUE 0.14585715 0.2885272 0.50552305 3.065958e-01
## TCTRUE -0.34705833 0.4861389 -0.71390769 2.376421e-01
## deforestationTRUE 0.18296402 0.4128509 0.44317216 3.288206e-01
## R1|R2 -0.09014790 1.0771711 -0.08368949 4.666517e-01
## R2|R3 4.32830466 1.0948499 3.95333146 3.853528e-05
## R3|R4 9.25964771 1.1864663 7.80439174 2.989460e-15stargazer((ctable), type="text", style="default", digits=2)##
## ======================================================
## Value Std. Error t value p value
## ------------------------------------------------------
## brickTRUE -0.69 0.56 -1.24 0.11
## woodTRUE 0.91 0.35 2.64 0.004
## ENTRUE 0.49 0.38 1.28 0.10
## TC_mature_soilTRUE 0.62 0.23 2.64 0.004
## T_constructionTRUE 0.26 0.36 0.73 0.23
## landfillTRUE 0.22 0.33 0.66 0.26
## leakTRUE -0.48 0.24 -1.99 0.02
## garbageTRUE -0.06 0.29 -0.20 0.42
## crackTRUE 1.96 0.33 5.90 0
## leaning_wallTRUE 1.59 0.49 3.24 0.001
## treeTRUE 0.02 0.24 0.10 0.46
## downward_floorTRUE 1.17 0.36 3.27 0.001
## tiltedTRUE 0.77 0.31 2.51 0.01
## ground_vegTRUE 0.70 0.27 2.63 0.004
## scarsTRUE 3.60 0.35 10.25 0
## mixedTRUE -0.27 0.54 -0.51 0.30
## conc_rainfallTRUE 1.03 0.55 1.88 0.03
## wastewaterTRUE 0.57 0.24 2.37 0.01
## angleD 0.51 0.47 1.08 0.14
## angleE 0.74 0.54 1.38 0.08
## bananaTRUE 0.52 0.25 2.11 0.02
## drainage.L 1.24 0.28 4.42 0.0000
## drainage.Q -0.15 0.19 -0.81 0.21
## TC_saprolite_soilTRUE 0.15 0.29 0.51 0.31
## TCTRUE -0.35 0.49 -0.71 0.24
## deforestationTRUE 0.18 0.41 0.44 0.33
## R1| R2 -0.09 1.08 -0.08 0.47
## R2| R3 4.33 1.09 3.95 0.0000
## R3| R4 9.26 1.19 7.80 0
## ------------------------------------------------------par(mfrow=c(6,4))
plot.xmean.ordinaly (risk~ brick+ wood+ EN+ TC_mature_soil+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ mixed+ conc_rainfall+ wastewater+ angle+ banana+ drainage+ TC_saprolite_soil+ TC+ deforestation
,data=train.data, cr=TRUE , subn=FALSE , cex.lab=1.5, cex.axis=4, cex.sub=4, cex.main=4)
sf <- function (y) {
c('y>=1' = qlogis(mean(y>=1)),
'y>=2' = qlogis(mean(y>=2)),
'y>=3' = qlogis(mean(y>=3)),
'y>=4' = qlogis(mean(y>=4)))
}
s<-with(train.data, summary(as.numeric(risk)~brick+ wood+ EN+ TC_mature_soil+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ mixed+ conc_rainfall+ wastewater+ angle+ banana+ drainage+ TC_saprolite_soil+ TC+ deforestation,data=train.data
, fun=sf))
s## as.numeric(risk) N= 529 , 1 Missing
##
## +-----------------+---+---+----+----------+------------+----------+
## | | |N |y>=1|y>=2 |y>=3 |y>=4 |
## +-----------------+---+---+----+----------+------------+----------+
## |brick |No | 33|Inf | 2.7408400| 0.980829253|-0.6931472|
## | |Yes|496|Inf | 2.3048048|-0.064538521|-2.0414481|
## +-----------------+---+---+----+----------+------------+----------+
## |wood |No |457|Inf | 2.2143609|-0.171094494|-2.1899518|
## | |Yes| 72|Inf | 3.5553481| 1.174119841|-0.8209806|
## +-----------------+---+---+----+----------+------------+----------+
## |EN |No |336|Inf | 1.8666608|-0.498147166|-2.3595519|
## | |Yes|193|Inf | 4.5591262| 0.894629235|-1.3733910|
## +-----------------+---+---+----+----------+------------+----------+
## |TC_mature_soil |No |264|Inf | 1.9810015|-0.243622083|-2.2141741|
## | |Yes|265|Inf | 2.8134107| 0.235038339|-1.6695022|
## +-----------------+---+---+----+----------+------------+----------+
## |T_construction |No |213|Inf | 1.6610253|-0.822825004|-3.0106209|
## | |Yes|316|Inf | 3.1487834| 0.531130890|-1.4925166|
## +-----------------+---+---+----+----------+------------+----------+
## |landfill |No |328|Inf | 1.8132657|-0.550046337|-2.5848176|
## | |Yes|201|Inf | 5.2983174| 0.926762032|-1.2431935|
## +-----------------+---+---+----+----------+------------+----------+
## |leak |No |341|Inf | 1.9627740|-0.253549066|-2.3385955|
## | |Yes|188|Inf | 3.6000482| 0.454472687|-1.3730491|
## +-----------------+---+---+----+----------+------------+----------+
## |garbage |No |353|Inf | 2.2067033|-0.198953957|-2.3057052|
## | |Yes|176|Inf | 2.6149598| 0.391280473|-1.3581235|
## +-----------------+---+---+----+----------+------------+----------+
## |crack |No |442|Inf | 2.1772738|-0.338023827|-2.6561518|
## | |Yes| 87|Inf | 3.7495041| 2.436116486|-0.2076394|
## +-----------------+---+---+----+----------+------------+----------+
## |leaning_wall |No |499|Inf | 2.2635346|-0.100284363|-2.0884914|
## | |Yes| 30|Inf | Inf| 2.197224577|-0.2682640|
## +-----------------+---+---+----+----------+------------+----------+
## |tree |No |209|Inf | 1.6739764|-0.538996501|-2.1919195|
## | |Yes|320|Inf | 3.0845278| 0.340759489|-1.7593242|
## +-----------------+---+---+----+----------+------------+----------+
## |downward_floor |No |467|Inf | 2.1901071|-0.254034038|-2.2885333|
## | |Yes| 62|Inf | Inf| 3.401197382|-0.3920421|
## +-----------------+---+---+----+----------+------------+----------+
## |tilted |No |423|Inf | 2.0794415|-0.437088297|-2.4696392|
## | |Yes|106|Inf | Inf| 2.505525937|-0.7073318|
## +-----------------+---+---+----+----------+------------+----------+
## |ground_veg |No |160|Inf | 1.2729657|-1.132228899|-2.5123056|
## | |Yes|369|Inf | 3.3928291| 0.446287103|-1.7208515|
## +-----------------+---+---+----+----------+------------+----------+
## |scars |No |328|Inf | 1.8132657|-1.286396208|-4.3944492|
## | |Yes|201|Inf | 5.2983174| 3.183248647|-0.7610978|
## +-----------------+---+---+----+----------+------------+----------+
## |mixed |No |494|Inf | 2.3003604|-0.064799993|-1.9787000|
## | |Yes| 35|Inf | 2.8033604| 0.916290732|-1.2163953|
## +-----------------+---+---+----+----------+------------+----------+
## |conc_rainfall |No | 20|Inf |-0.2006707|-2.944438979| -Inf|
## | |Yes|509|Inf | 2.5755765| 0.066822496|-1.8695372|
## +-----------------+---+---+----+----------+------------+----------+
## |wastewater |No |205|Inf | 1.6875557|-0.529079064|-2.7777835|
## | |Yes|324|Inf | 3.0252911| 0.323787077|-1.5656353|
## +-----------------+---+---+----+----------+------------+----------+
## |angle |C | 30|Inf | Inf|-0.546543706|-3.3672958|
## | |D |131|Inf | 4.1666652| 0.970357953|-1.3958638|
## | |E |368|Inf | 1.9709898|-0.284512498|-2.0763881|
## +-----------------+---+---+----+----------+------------+----------+
## |banana |No |360|Inf | 1.9459101|-0.290923566|-2.1667344|
## | |Yes|169|Inf | 4.4248466| 0.622942922|-1.4932665|
## +-----------------+---+---+----+----------+------------+----------+
## |drainage |Y | 71|Inf | 0.6720938|-2.063693185|-4.2484952|
## | |P |230|Inf | 2.4662145|-0.496936512|-2.5933873|
## | |N |228|Inf | 3.7977339| 1.052361271|-1.2443241|
## +-----------------+---+---+----+----------+------------+----------+
## |TC_saprolite_soil|No |443|Inf | 2.2047718|-0.076787143|-2.0845306|
## | |Yes| 86|Inf | 3.3202283| 0.376477571|-1.2602536|
## +-----------------+---+---+----+----------+------------+----------+
## |TC |No | 31|Inf | Inf| 0.741937345|-1.4271164|
## | |Yes|498|Inf | 2.2613197|-0.048202102|-1.9505079|
## +-----------------+---+---+----+----------+------------+----------+
## |deforestation |No |492|Inf | 2.3978953| 0.048790164|-1.8823967|
## | |Yes| 37|Inf | 1.6422277|-0.733969175|-2.4277482|
## +-----------------+---+---+----+----------+------------+----------+
## |Overall | |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +-----------------+---+---+----+----------+------------+----------+plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=1, cex.axis=2, cex.sub=1)
Equation 3
- parameters okay and so/so
- porportion
- p-value based equation 2 > 0.5
f3 <- lrm(risk ~ angle +building + EN + DepTaludeAterro+ DepTaludeCorte+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall_water+ wastewater+ tree + TC , data=train.data, x=TRUE , y=TRUE) stargazer(anova(f3), type=“text”, style=“default”)
# x=TRUE, y=TRUE used by resid() below
eq_OLR_03 <- polr(risk ~ wood+ TC_mature_soil+ T_construction+ landfill+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage, data=train.data
, method = "logistic", Hess = TRUE)
ctable <- coef(summary(eq_OLR_03))
p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )
ctable## Value Std. Error t value p value
## woodTRUE 0.85830150 0.3260349 2.6325445 4.237396e-03
## TC_mature_soilTRUE 0.53837735 0.2212512 2.4333302 7.480325e-03
## T_constructionTRUE 0.23815042 0.2856075 0.8338381 2.021861e-01
## landfillTRUE 0.23432679 0.2913347 0.8043216 2.106056e-01
## crackTRUE 1.98220581 0.3210796 6.1735656 3.338341e-10
## leaning_wallTRUE 1.59155919 0.4866439 3.2704802 5.368253e-04
## treeTRUE 0.06975326 0.2272725 0.3069146 3.794542e-01
## downward_floorTRUE 1.03553738 0.3474878 2.9800687 1.440919e-03
## tiltedTRUE 0.71385198 0.2973872 2.4004128 8.188297e-03
## ground_vegTRUE 0.69756485 0.2593014 2.6901704 3.570777e-03
## scarsTRUE 3.58807311 0.3472203 10.3337087 2.480240e-25
## conc_rainfallTRUE 1.02117774 0.5383283 1.8969422 2.891778e-02
## wastewaterTRUE 0.51860275 0.2315241 2.2399510 1.254705e-02
## bananaTRUE 0.48288701 0.2367276 2.0398426 2.068300e-02
## drainage.L 1.19539241 0.2743370 4.3573875 6.581205e-06
## drainage.Q -0.11222219 0.1844603 -0.6083813 2.714673e-01
## R1|R2 0.21961839 0.5230872 0.4198505 3.372973e-01
## R2|R3 4.54030875 0.5819245 7.8022302 3.041129e-15
## R3|R4 9.39510325 0.7217471 13.0171687 4.886389e-39stargazer((ctable), type="text", style="default", digits = 2)##
## ===================================================
## Value Std. Error t value p value
## ---------------------------------------------------
## woodTRUE 0.86 0.33 2.63 0.004
## TC_mature_soilTRUE 0.54 0.22 2.43 0.01
## T_constructionTRUE 0.24 0.29 0.83 0.20
## landfillTRUE 0.23 0.29 0.80 0.21
## crackTRUE 1.98 0.32 6.17 0
## leaning_wallTRUE 1.59 0.49 3.27 0.001
## treeTRUE 0.07 0.23 0.31 0.38
## downward_floorTRUE 1.04 0.35 2.98 0.001
## tiltedTRUE 0.71 0.30 2.40 0.01
## ground_vegTRUE 0.70 0.26 2.69 0.004
## scarsTRUE 3.59 0.35 10.33 0
## conc_rainfallTRUE 1.02 0.54 1.90 0.03
## wastewaterTRUE 0.52 0.23 2.24 0.01
## bananaTRUE 0.48 0.24 2.04 0.02
## drainage.L 1.20 0.27 4.36 0.0000
## drainage.Q -0.11 0.18 -0.61 0.27
## R1| R2 0.22 0.52 0.42 0.34
## R2| R3 4.54 0.58 7.80 0
## R3| R4 9.40 0.72 13.02 0
## ---------------------------------------------------#print (f3 , latex =TRUE , coefs =5)par(mfrow=c(3,5))
plot.xmean.ordinaly (risk ~ wood+ TC_mature_soil+ T_construction+ landfill+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage,,
data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)
sf <- function (y) {
c('y>=1' = qlogis(mean(y>=1)),
'y>=2' = qlogis(mean(y>=2)),
'y>=3' = qlogis(mean(y>=3)),
'y>=4' = qlogis(mean(y>=4)))
}
s<-with(train.data, summary(as.numeric(risk)~wood+ TC_mature_soil+ T_construction+ landfill+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage, fun=sf))
s## as.numeric(risk) N= 529 , 1 Missing
##
## +--------------+---+---+----+----------+------------+----------+
## | | |N |y>=1|y>=2 |y>=3 |y>=4 |
## +--------------+---+---+----+----------+------------+----------+
## |wood |No |457|Inf | 2.2143609|-0.171094494|-2.1899518|
## | |Yes| 72|Inf | 3.5553481| 1.174119841|-0.8209806|
## +--------------+---+---+----+----------+------------+----------+
## |TC_mature_soil|No |264|Inf | 1.9810015|-0.243622083|-2.2141741|
## | |Yes|265|Inf | 2.8134107| 0.235038339|-1.6695022|
## +--------------+---+---+----+----------+------------+----------+
## |T_construction|No |213|Inf | 1.6610253|-0.822825004|-3.0106209|
## | |Yes|316|Inf | 3.1487834| 0.531130890|-1.4925166|
## +--------------+---+---+----+----------+------------+----------+
## |landfill |No |328|Inf | 1.8132657|-0.550046337|-2.5848176|
## | |Yes|201|Inf | 5.2983174| 0.926762032|-1.2431935|
## +--------------+---+---+----+----------+------------+----------+
## |crack |No |442|Inf | 2.1772738|-0.338023827|-2.6561518|
## | |Yes| 87|Inf | 3.7495041| 2.436116486|-0.2076394|
## +--------------+---+---+----+----------+------------+----------+
## |leaning_wall |No |499|Inf | 2.2635346|-0.100284363|-2.0884914|
## | |Yes| 30|Inf | Inf| 2.197224577|-0.2682640|
## +--------------+---+---+----+----------+------------+----------+
## |tree |No |209|Inf | 1.6739764|-0.538996501|-2.1919195|
## | |Yes|320|Inf | 3.0845278| 0.340759489|-1.7593242|
## +--------------+---+---+----+----------+------------+----------+
## |downward_floor|No |467|Inf | 2.1901071|-0.254034038|-2.2885333|
## | |Yes| 62|Inf | Inf| 3.401197382|-0.3920421|
## +--------------+---+---+----+----------+------------+----------+
## |tilted |No |423|Inf | 2.0794415|-0.437088297|-2.4696392|
## | |Yes|106|Inf | Inf| 2.505525937|-0.7073318|
## +--------------+---+---+----+----------+------------+----------+
## |ground_veg |No |160|Inf | 1.2729657|-1.132228899|-2.5123056|
## | |Yes|369|Inf | 3.3928291| 0.446287103|-1.7208515|
## +--------------+---+---+----+----------+------------+----------+
## |scars |No |328|Inf | 1.8132657|-1.286396208|-4.3944492|
## | |Yes|201|Inf | 5.2983174| 3.183248647|-0.7610978|
## +--------------+---+---+----+----------+------------+----------+
## |conc_rainfall |No | 20|Inf |-0.2006707|-2.944438979| -Inf|
## | |Yes|509|Inf | 2.5755765| 0.066822496|-1.8695372|
## +--------------+---+---+----+----------+------------+----------+
## |wastewater |No |205|Inf | 1.6875557|-0.529079064|-2.7777835|
## | |Yes|324|Inf | 3.0252911| 0.323787077|-1.5656353|
## +--------------+---+---+----+----------+------------+----------+
## |banana |No |360|Inf | 1.9459101|-0.290923566|-2.1667344|
## | |Yes|169|Inf | 4.4248466| 0.622942922|-1.4932665|
## +--------------+---+---+----+----------+------------+----------+
## |drainage |Y | 71|Inf | 0.6720938|-2.063693185|-4.2484952|
## | |P |230|Inf | 2.4662145|-0.496936512|-2.5933873|
## | |N |228|Inf | 3.7977339| 1.052361271|-1.2443241|
## +--------------+---+---+----+----------+------------+----------+
## |Overall | |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +--------------+---+---+----+----------+------------+----------+plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.6, cex.axis=0.6, cex.sub=0.6)
Equation 4
- p-value equation 3 > 0.05 (banana, DepTaludeCorte)
- consider proportion
- paremeters okay & so/so
- fashion order
f4 <- lrm(risk ~ building + EN
+ DepEncNatural
+ crack + leaning_wall + scars + downward_floor +tilted + conc_rainfall_water + wastewater + drainage + TC_mature_Soil + TC + +ground_veg
,data=train.data, x=TRUE , y=TRUE) # x=TRUE, y=TRUE used by resid() below #print (f4 , latex =TRUE , coefs =5) stargazer(anova(f4), type=“text”, style=“default”)
eq_OLR_04 <- polr(risk~ wood+ TC_mature_soil+ T_construction+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage
, data= train.data
, method = "logistic", Hess = TRUE)
p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- coef(summary(eq_OLR_04))
ctable <- cbind(ctable, "p value" = p )## Warning in cbind(ctable, `p value` = p): number of rows of result is not a
## multiple of vector length (arg 2)ctable## Value Std. Error t value p value
## woodTRUE 0.84885165 0.3253134 2.6093349 4.237396e-03
## TC_mature_soilTRUE 0.51097907 0.2183982 2.3396666 7.480325e-03
## T_constructionTRUE 0.37433338 0.2301013 1.6268197 2.021861e-01
## crackTRUE 2.00201945 0.3206195 6.2442224 2.106056e-01
## leaning_wallTRUE 1.56548583 0.4865142 3.2177599 3.338341e-10
## treeTRUE 0.05851965 0.2268813 0.2579307 5.368253e-04
## downward_floorTRUE 1.06459936 0.3454842 3.0814700 3.794542e-01
## tiltedTRUE 0.75370913 0.2931559 2.5710184 1.440919e-03
## ground_vegTRUE 0.71855215 0.2579941 2.7851500 8.188297e-03
## scarsTRUE 3.59428167 0.3471872 10.3525762 3.570777e-03
## conc_rainfallTRUE 1.03576481 0.5385751 1.9231577 2.480240e-25
## wastewaterTRUE 0.49091118 0.2289552 2.1441361 2.891778e-02
## bananaTRUE 0.48480360 0.2366466 2.0486400 1.254705e-02
## drainage.L 1.20804445 0.2738221 4.4117863 2.068300e-02
## drainage.Q -0.11228051 0.1843756 -0.6089771 6.581205e-06
## R1|R2 0.22549862 0.5237415 0.4305533 2.714673e-01
## R2|R3 4.54218190 0.5826881 7.7952200 3.372973e-01
## R3|R4 9.38943920 0.7219268 13.0060820 3.041129e-15stargazer((ctable), type="text", style="default", digits=2)##
## ===================================================
## Value Std. Error t value p value
## ---------------------------------------------------
## woodTRUE 0.85 0.33 2.61 0.004
## TC_mature_soilTRUE 0.51 0.22 2.34 0.01
## T_constructionTRUE 0.37 0.23 1.63 0.20
## crackTRUE 2.00 0.32 6.24 0.21
## leaning_wallTRUE 1.57 0.49 3.22 0
## treeTRUE 0.06 0.23 0.26 0.001
## downward_floorTRUE 1.06 0.35 3.08 0.38
## tiltedTRUE 0.75 0.29 2.57 0.001
## ground_vegTRUE 0.72 0.26 2.79 0.01
## scarsTRUE 3.59 0.35 10.35 0.004
## conc_rainfallTRUE 1.04 0.54 1.92 0
## wastewaterTRUE 0.49 0.23 2.14 0.03
## bananaTRUE 0.48 0.24 2.05 0.01
## drainage.L 1.21 0.27 4.41 0.02
## drainage.Q -0.11 0.18 -0.61 0.0000
## R1| R2 0.23 0.52 0.43 0.27
## R2| R3 4.54 0.58 7.80 0.34
## R3| R4 9.39 0.72 13.01 0
## ---------------------------------------------------par(mfrow=c(4,4))
plot.xmean.ordinaly (risk ~ wood+ TC_mature_soil+ T_construction+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage
,data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)
sf <- function (y) {
c('y>=1' = qlogis(mean(y>=1)),
'y>=2' = qlogis(mean(y>=2)),
'y>=3' = qlogis(mean(y>=3)),
'y>=4' = qlogis(mean(y>=4)))
}
s<-with(train.data, summary(as.numeric(risk)~wood+ TC_mature_soil+ T_construction+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage
, fun=sf))
s## as.numeric(risk) N= 529 , 1 Missing
##
## +--------------+---+---+----+----------+------------+----------+
## | | |N |y>=1|y>=2 |y>=3 |y>=4 |
## +--------------+---+---+----+----------+------------+----------+
## |wood |No |457|Inf | 2.2143609|-0.171094494|-2.1899518|
## | |Yes| 72|Inf | 3.5553481| 1.174119841|-0.8209806|
## +--------------+---+---+----+----------+------------+----------+
## |TC_mature_soil|No |264|Inf | 1.9810015|-0.243622083|-2.2141741|
## | |Yes|265|Inf | 2.8134107| 0.235038339|-1.6695022|
## +--------------+---+---+----+----------+------------+----------+
## |T_construction|No |213|Inf | 1.6610253|-0.822825004|-3.0106209|
## | |Yes|316|Inf | 3.1487834| 0.531130890|-1.4925166|
## +--------------+---+---+----+----------+------------+----------+
## |crack |No |442|Inf | 2.1772738|-0.338023827|-2.6561518|
## | |Yes| 87|Inf | 3.7495041| 2.436116486|-0.2076394|
## +--------------+---+---+----+----------+------------+----------+
## |leaning_wall |No |499|Inf | 2.2635346|-0.100284363|-2.0884914|
## | |Yes| 30|Inf | Inf| 2.197224577|-0.2682640|
## +--------------+---+---+----+----------+------------+----------+
## |tree |No |209|Inf | 1.6739764|-0.538996501|-2.1919195|
## | |Yes|320|Inf | 3.0845278| 0.340759489|-1.7593242|
## +--------------+---+---+----+----------+------------+----------+
## |downward_floor|No |467|Inf | 2.1901071|-0.254034038|-2.2885333|
## | |Yes| 62|Inf | Inf| 3.401197382|-0.3920421|
## +--------------+---+---+----+----------+------------+----------+
## |tilted |No |423|Inf | 2.0794415|-0.437088297|-2.4696392|
## | |Yes|106|Inf | Inf| 2.505525937|-0.7073318|
## +--------------+---+---+----+----------+------------+----------+
## |ground_veg |No |160|Inf | 1.2729657|-1.132228899|-2.5123056|
## | |Yes|369|Inf | 3.3928291| 0.446287103|-1.7208515|
## +--------------+---+---+----+----------+------------+----------+
## |scars |No |328|Inf | 1.8132657|-1.286396208|-4.3944492|
## | |Yes|201|Inf | 5.2983174| 3.183248647|-0.7610978|
## +--------------+---+---+----+----------+------------+----------+
## |conc_rainfall |No | 20|Inf |-0.2006707|-2.944438979| -Inf|
## | |Yes|509|Inf | 2.5755765| 0.066822496|-1.8695372|
## +--------------+---+---+----+----------+------------+----------+
## |wastewater |No |205|Inf | 1.6875557|-0.529079064|-2.7777835|
## | |Yes|324|Inf | 3.0252911| 0.323787077|-1.5656353|
## +--------------+---+---+----+----------+------------+----------+
## |banana |No |360|Inf | 1.9459101|-0.290923566|-2.1667344|
## | |Yes|169|Inf | 4.4248466| 0.622942922|-1.4932665|
## +--------------+---+---+----+----------+------------+----------+
## |drainage |Y | 71|Inf | 0.6720938|-2.063693185|-4.2484952|
## | |P |230|Inf | 2.4662145|-0.496936512|-2.5933873|
## | |N |228|Inf | 3.7977339| 1.052361271|-1.2443241|
## +--------------+---+---+----+----------+------------+----------+
## |Overall | |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +--------------+---+---+----+----------+------------+----------+plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.7, cex.axis=0.5, cex.sub=0.5)
Equation 5 - Based on Equation 1
- based on Eq 1
- less p-value > 0.10 (
# x=TRUE, y=TRUE used by resid() below
#print (f1 , latex =TRUE , coefs =5)
#stargazer(anova(f1), type="text", style="default")
eq_OLR_05 <- polr(risk ~ brick+ wood+ TC_mature_soil+ T_construction+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall+ wastewater+ ground_veg, data= train.data
, method = "logistic", Hess = TRUE)
ctable <- coef(summary(eq_OLR_05))
p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )
ctable## Value Std. Error t value p value
## brickTRUE -0.4370018 0.4423303 -0.9879536 1.615877e-01
## woodTRUE 1.0207921 0.3198713 3.1912584 7.082727e-04
## TC_mature_soilTRUE 0.6200433 0.2151391 2.8820584 1.975433e-03
## T_constructionTRUE 0.3959872 0.2234254 1.7723462 3.816855e-02
## crackTRUE 1.8795793 0.3131351 6.0024541 9.717861e-10
## leaning_wallTRUE 1.4240258 0.4758818 2.9923935 1.383996e-03
## scarsTRUE 3.6689232 0.3436234 10.6771644 6.509383e-27
## downward_floorTRUE 1.2784517 0.3427868 3.7295824 9.589869e-05
## tiltedTRUE 0.9078147 0.2914811 3.1144890 9.213191e-04
## conc_rainfallTRUE 1.5125790 0.5094811 2.9688618 1.494525e-03
## wastewaterTRUE 0.7065394 0.2212379 3.1935729 7.026194e-04
## ground_vegTRUE 1.0266533 0.2359413 4.3513073 6.766411e-06
## R1|R2 0.5181656 0.6575352 0.7880423 2.153360e-01
## R2|R3 4.5736433 0.7141022 6.4047463 7.530980e-11
## R3|R4 9.3408231 0.8211153 11.3757745 2.760526e-30stargazer((ctable), type="text", style="default", digits = 2)##
## ===================================================
## Value Std. Error t value p value
## ---------------------------------------------------
## brickTRUE -0.44 0.44 -0.99 0.16
## woodTRUE 1.02 0.32 3.19 0.001
## TC_mature_soilTRUE 0.62 0.22 2.88 0.002
## T_constructionTRUE 0.40 0.22 1.77 0.04
## crackTRUE 1.88 0.31 6.00 0
## leaning_wallTRUE 1.42 0.48 2.99 0.001
## scarsTRUE 3.67 0.34 10.68 0
## downward_floorTRUE 1.28 0.34 3.73 0.0001
## tiltedTRUE 0.91 0.29 3.11 0.001
## conc_rainfallTRUE 1.51 0.51 2.97 0.001
## wastewaterTRUE 0.71 0.22 3.19 0.001
## ground_vegTRUE 1.03 0.24 4.35 0.0000
## R1| R2 0.52 0.66 0.79 0.22
## R2| R3 4.57 0.71 6.40 0
## R3| R4 9.34 0.82 11.38 0
## ---------------------------------------------------par(mfrow=c(3,4))
plot.xmean.ordinaly (risk ~ brick+ wood+ TC_mature_soil+ T_construction+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall+ wastewater+ ground_veg
,data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)
sf <- function (y) {
c('y>=1' = qlogis(mean(y>=1)),
'y>=2' = qlogis(mean(y>=2)),
'y>=3' = qlogis(mean(y>=3)),
'y>=4' = qlogis(mean(y>=4)))
}
s<-with(train.data, summary(as.numeric(risk)~brick+ wood+ TC_mature_soil+ T_construction+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall+ wastewater+ ground_veg
, fun=sf))
s## as.numeric(risk) N= 529 , 1 Missing
##
## +--------------+---+---+----+----------+------------+----------+
## | | |N |y>=1|y>=2 |y>=3 |y>=4 |
## +--------------+---+---+----+----------+------------+----------+
## |brick |No | 33|Inf | 2.7408400| 0.980829253|-0.6931472|
## | |Yes|496|Inf | 2.3048048|-0.064538521|-2.0414481|
## +--------------+---+---+----+----------+------------+----------+
## |wood |No |457|Inf | 2.2143609|-0.171094494|-2.1899518|
## | |Yes| 72|Inf | 3.5553481| 1.174119841|-0.8209806|
## +--------------+---+---+----+----------+------------+----------+
## |TC_mature_soil|No |264|Inf | 1.9810015|-0.243622083|-2.2141741|
## | |Yes|265|Inf | 2.8134107| 0.235038339|-1.6695022|
## +--------------+---+---+----+----------+------------+----------+
## |T_construction|No |213|Inf | 1.6610253|-0.822825004|-3.0106209|
## | |Yes|316|Inf | 3.1487834| 0.531130890|-1.4925166|
## +--------------+---+---+----+----------+------------+----------+
## |crack |No |442|Inf | 2.1772738|-0.338023827|-2.6561518|
## | |Yes| 87|Inf | 3.7495041| 2.436116486|-0.2076394|
## +--------------+---+---+----+----------+------------+----------+
## |leaning_wall |No |499|Inf | 2.2635346|-0.100284363|-2.0884914|
## | |Yes| 30|Inf | Inf| 2.197224577|-0.2682640|
## +--------------+---+---+----+----------+------------+----------+
## |scars |No |328|Inf | 1.8132657|-1.286396208|-4.3944492|
## | |Yes|201|Inf | 5.2983174| 3.183248647|-0.7610978|
## +--------------+---+---+----+----------+------------+----------+
## |downward_floor|No |467|Inf | 2.1901071|-0.254034038|-2.2885333|
## | |Yes| 62|Inf | Inf| 3.401197382|-0.3920421|
## +--------------+---+---+----+----------+------------+----------+
## |tilted |No |423|Inf | 2.0794415|-0.437088297|-2.4696392|
## | |Yes|106|Inf | Inf| 2.505525937|-0.7073318|
## +--------------+---+---+----+----------+------------+----------+
## |conc_rainfall |No | 20|Inf |-0.2006707|-2.944438979| -Inf|
## | |Yes|509|Inf | 2.5755765| 0.066822496|-1.8695372|
## +--------------+---+---+----+----------+------------+----------+
## |wastewater |No |205|Inf | 1.6875557|-0.529079064|-2.7777835|
## | |Yes|324|Inf | 3.0252911| 0.323787077|-1.5656353|
## +--------------+---+---+----+----------+------------+----------+
## |ground_veg |No |160|Inf | 1.2729657|-1.132228899|-2.5123056|
## | |Yes|369|Inf | 3.3928291| 0.446287103|-1.7208515|
## +--------------+---+---+----+----------+------------+----------+
## |Overall | |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +--------------+---+---+----+----------+------------+----------+plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.7, cex.axis=0.5, cex.sub=0.5)
OLR Equation 6
# x=TRUE, y=TRUE used by resid() below
#print (f1 , latex =TRUE , coefs =5)
#stargazer(anova(f1), type="text", style="default")
eq_OLR_06 <- polr(risk ~ brick+ wood+ mixed+ EN+ TC+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ tilted+ angle+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana, data= train.data
, method = "logistic", Hess = TRUE)
ctable <- coef(summary(eq_OLR_06))
p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )
ctable## Value Std. Error t value p value
## brickTRUE -0.942889607 0.5407213 -1.74376275 4.060023e-02
## woodTRUE 0.859549174 0.3217759 2.67126613 3.778285e-03
## mixedTRUE -0.023613745 0.5181062 -0.04557704 4.818237e-01
## ENTRUE 0.644194626 0.3670085 1.75525824 3.960757e-02
## TCTRUE 0.015391373 0.4503644 0.03417537 4.863687e-01
## T_constructionTRUE 0.254507322 0.3403651 0.74774789 2.273061e-01
## landfillTRUE 0.268683843 0.3128988 0.85869241 1.952551e-01
## leakTRUE -0.171162134 0.2287266 -0.74832632 2.271317e-01
## garbageTRUE -0.009183685 0.2798431 -0.03281727 4.869102e-01
## crackTRUE 1.764284254 0.3153780 5.59418920 1.108276e-08
## leaning_wallTRUE 1.458922795 0.4845785 3.01070468 1.303211e-03
## treeTRUE 0.006606937 0.2279786 0.02898051 4.884401e-01
## tiltedTRUE 0.902937852 0.2955678 3.05492585 1.125581e-03
## angleD 0.554697232 0.4602188 1.20529025 1.140456e-01
## angleE 0.858086750 0.5233230 1.63968862 5.053496e-02
## ground_vegTRUE 0.841494315 0.2564667 3.28110510 5.170060e-04
## scarsTRUE 3.762910122 0.3462670 10.86707843 8.270655e-28
## conc_rainfallTRUE 1.834223348 0.5161322 3.55378579 1.898642e-04
## wastewaterTRUE 0.681293972 0.2246589 3.03257111 1.212400e-03
## bananaTRUE 0.656452968 0.2391610 2.74481643 3.027238e-03
## R1|R2 0.932373154 1.0307023 0.90459985 1.828387e-01
## R2|R3 4.951454233 1.0613488 4.66524701 1.541233e-06
## R3|R4 9.699844620 1.1534212 8.40962923 2.056544e-17stargazer((ctable), type="text", style="default", digits = 2)##
## ===================================================
## Value Std. Error t value p value
## ---------------------------------------------------
## brickTRUE -0.94 0.54 -1.74 0.04
## woodTRUE 0.86 0.32 2.67 0.004
## mixedTRUE -0.02 0.52 -0.05 0.48
## ENTRUE 0.64 0.37 1.76 0.04
## TCTRUE 0.02 0.45 0.03 0.49
## T_constructionTRUE 0.25 0.34 0.75 0.23
## landfillTRUE 0.27 0.31 0.86 0.20
## leakTRUE -0.17 0.23 -0.75 0.23
## garbageTRUE -0.01 0.28 -0.03 0.49
## crackTRUE 1.76 0.32 5.59 0
## leaning_wallTRUE 1.46 0.48 3.01 0.001
## treeTRUE 0.01 0.23 0.03 0.49
## tiltedTRUE 0.90 0.30 3.05 0.001
## angleD 0.55 0.46 1.21 0.11
## angleE 0.86 0.52 1.64 0.05
## ground_vegTRUE 0.84 0.26 3.28 0.001
## scarsTRUE 3.76 0.35 10.87 0
## conc_rainfallTRUE 1.83 0.52 3.55 0.0002
## wastewaterTRUE 0.68 0.22 3.03 0.001
## bananaTRUE 0.66 0.24 2.74 0.003
## R1| R2 0.93 1.03 0.90 0.18
## R2| R3 4.95 1.06 4.67 0.0000
## R3| R4 9.70 1.15 8.41 0
## ---------------------------------------------------par(mfrow=c(5,4))
plot.xmean.ordinaly (risk ~ brick+ wood+ mixed+ EN+ TC+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ tilted+ angle+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana
,data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)
sf <- function (y) {
c('y>=1' = qlogis(mean(y>=1)),
'y>=2' = qlogis(mean(y>=2)),
'y>=3' = qlogis(mean(y>=3)),
'y>=4' = qlogis(mean(y>=4)))
}
s<-with(train.data, summary(as.numeric(risk)~brick+ wood+ mixed+ EN+ TC+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ tilted+ angle+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana
, fun=sf))
s## as.numeric(risk) N= 529 , 1 Missing
##
## +--------------+---+---+----+----------+------------+----------+
## | | |N |y>=1|y>=2 |y>=3 |y>=4 |
## +--------------+---+---+----+----------+------------+----------+
## |brick |No | 33|Inf | 2.7408400| 0.980829253|-0.6931472|
## | |Yes|496|Inf | 2.3048048|-0.064538521|-2.0414481|
## +--------------+---+---+----+----------+------------+----------+
## |wood |No |457|Inf | 2.2143609|-0.171094494|-2.1899518|
## | |Yes| 72|Inf | 3.5553481| 1.174119841|-0.8209806|
## +--------------+---+---+----+----------+------------+----------+
## |mixed |No |494|Inf | 2.3003604|-0.064799993|-1.9787000|
## | |Yes| 35|Inf | 2.8033604| 0.916290732|-1.2163953|
## +--------------+---+---+----+----------+------------+----------+
## |EN |No |336|Inf | 1.8666608|-0.498147166|-2.3595519|
## | |Yes|193|Inf | 4.5591262| 0.894629235|-1.3733910|
## +--------------+---+---+----+----------+------------+----------+
## |TC |No | 31|Inf | Inf| 0.741937345|-1.4271164|
## | |Yes|498|Inf | 2.2613197|-0.048202102|-1.9505079|
## +--------------+---+---+----+----------+------------+----------+
## |T_construction|No |213|Inf | 1.6610253|-0.822825004|-3.0106209|
## | |Yes|316|Inf | 3.1487834| 0.531130890|-1.4925166|
## +--------------+---+---+----+----------+------------+----------+
## |landfill |No |328|Inf | 1.8132657|-0.550046337|-2.5848176|
## | |Yes|201|Inf | 5.2983174| 0.926762032|-1.2431935|
## +--------------+---+---+----+----------+------------+----------+
## |leak |No |341|Inf | 1.9627740|-0.253549066|-2.3385955|
## | |Yes|188|Inf | 3.6000482| 0.454472687|-1.3730491|
## +--------------+---+---+----+----------+------------+----------+
## |garbage |No |353|Inf | 2.2067033|-0.198953957|-2.3057052|
## | |Yes|176|Inf | 2.6149598| 0.391280473|-1.3581235|
## +--------------+---+---+----+----------+------------+----------+
## |crack |No |442|Inf | 2.1772738|-0.338023827|-2.6561518|
## | |Yes| 87|Inf | 3.7495041| 2.436116486|-0.2076394|
## +--------------+---+---+----+----------+------------+----------+
## |leaning_wall |No |499|Inf | 2.2635346|-0.100284363|-2.0884914|
## | |Yes| 30|Inf | Inf| 2.197224577|-0.2682640|
## +--------------+---+---+----+----------+------------+----------+
## |tree |No |209|Inf | 1.6739764|-0.538996501|-2.1919195|
## | |Yes|320|Inf | 3.0845278| 0.340759489|-1.7593242|
## +--------------+---+---+----+----------+------------+----------+
## |tilted |No |423|Inf | 2.0794415|-0.437088297|-2.4696392|
## | |Yes|106|Inf | Inf| 2.505525937|-0.7073318|
## +--------------+---+---+----+----------+------------+----------+
## |angle |C | 30|Inf | Inf|-0.546543706|-3.3672958|
## | |D |131|Inf | 4.1666652| 0.970357953|-1.3958638|
## | |E |368|Inf | 1.9709898|-0.284512498|-2.0763881|
## +--------------+---+---+----+----------+------------+----------+
## |ground_veg |No |160|Inf | 1.2729657|-1.132228899|-2.5123056|
## | |Yes|369|Inf | 3.3928291| 0.446287103|-1.7208515|
## +--------------+---+---+----+----------+------------+----------+
## |scars |No |328|Inf | 1.8132657|-1.286396208|-4.3944492|
## | |Yes|201|Inf | 5.2983174| 3.183248647|-0.7610978|
## +--------------+---+---+----+----------+------------+----------+
## |conc_rainfall |No | 20|Inf |-0.2006707|-2.944438979| -Inf|
## | |Yes|509|Inf | 2.5755765| 0.066822496|-1.8695372|
## +--------------+---+---+----+----------+------------+----------+
## |wastewater |No |205|Inf | 1.6875557|-0.529079064|-2.7777835|
## | |Yes|324|Inf | 3.0252911| 0.323787077|-1.5656353|
## +--------------+---+---+----+----------+------------+----------+
## |banana |No |360|Inf | 1.9459101|-0.290923566|-2.1667344|
## | |Yes|169|Inf | 4.4248466| 0.622942922|-1.4932665|
## +--------------+---+---+----+----------+------------+----------+
## |Overall | |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +--------------+---+---+----+----------+------------+----------+plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.7, cex.axis=0.5, cex.sub=0.5)
Predicion on test data Eq 1: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html
predictedLevel1 <- predict(eq_OLR_01, test.data) # predict the levels directly
predictedScores1 <- predict(eq_OLR_01, test.data, type="p")
# predict the probabilites
## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel1)## predictedLevel1
## R1 R2 R3 R4
## R1 3 15 1 0
## R2 0 88 5 0
## R3 0 12 61 11
## R4 0 0 12 16p1 <- mean(as.character(test.data$risk) != as.character(predictedLevel1)) #misclassification error
p1 ## [1] 0.25Predicion on test data Eq 2: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html
predictedLevel2 <- predict(eq_OLR_02, test.data) # predict the levels directly
## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel2)## predictedLevel2
## R1 R2 R3 R4
## R1 5 14 0 0
## R2 3 85 5 0
## R3 0 12 59 13
## R4 0 0 13 15p2 <- mean(as.character(test.data$risk) != as.character(predictedLevel2))
p2## [1] 0.2678571Predicion on test data Eq 3: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html
predictedLevel3 <- predict(eq_OLR_03, test.data) # predict the levels directly
predictedScores1 <- predict(eq_OLR_03, test.data, type="p")
# predict the probabilites
## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel3)## predictedLevel3
## R1 R2 R3 R4
## R1 5 13 1 0
## R2 4 82 7 0
## R3 0 12 62 10
## R4 0 0 11 17p3 <- mean(as.character(test.data$risk) != as.character(predictedLevel3))
p3## [1] 0.2589286Predicion on test data Eq 4: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html
predictedLevel4 <- predict(eq_OLR_04, test.data) # predict the levels directly
predictedScores1 <- predict(eq_OLR_04, test.data, type="p")
# predict the probabilites
## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel4)## predictedLevel4
## R1 R2 R3 R4
## R1 5 14 0 0
## R2 4 84 5 0
## R3 0 11 63 10
## R4 0 0 11 17p4 <- mean(as.character(test.data$risk) != as.character(predictedLevel4))
p4## [1] 0.2455357Predicion on test data Eq 5: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html
predictedLevel5 <- predict(eq_OLR_05, test.data) # predict the levels directly
predictedScores5 <- predict(eq_OLR_05, test.data, type="p")
# predict the probabilites
## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel5)## predictedLevel5
## R1 R2 R3 R4
## R1 2 16 1 0
## R2 0 88 5 0
## R3 0 12 59 13
## R4 0 0 11 17p5 <- mean(as.character(test.data$risk) != as.character(predictedLevel5))
p5## [1] 0.2589286Predicion on test data Eq 6: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html
predictedLevel6 <- predict(eq_OLR_06, test.data) # predict the levels directly
predictedScores6 <- predict(eq_OLR_06, test.data, type="p")
# predict the probabilites
## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel6)## predictedLevel6
## R1 R2 R3 R4
## R1 3 15 1 0
## R2 1 87 5 0
## R3 0 14 63 7
## R4 0 0 11 17p6 <- mean(as.character(test.data$risk) != as.character(predictedLevel6))
p6## [1] 0.2410714Predicion on test data Eq 7: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html
#Table
df2 <- data.frame(
"Equations"=c(1:6),
"Predicted"=c(1-p1,
1-p2,
1-p3,
1-p4,
1-p5,
1-p6
)
)
df2## Equations Predicted
## 1 1 0.7500000
## 2 2 0.7321429
## 3 3 0.7410714
## 4 4 0.7544643
## 5 5 0.7410714
## 6 6 0.7589286